Gravity profiles interpretation applying a metaheuristic particle optimization algorithm of mineralized bodies resembled by finite elements

The interpretation of gravity anomalies is crucial for identifying subsurface mineralized targets and understanding the density variations between the targets and the surrounding structures. To confirm the presence of ore and mineral targets, simple geometric bodies are often used. One of the commonly used global metaheuristic algorithms for gravity data analysis is the particle optimization algorithm. In this study, we employed this method to determine the parameters of buried bodies that resemble finite vertical cylinders by inferring gravity anomalies profiles (amplitude coefficient, depth to top, depth to bottom, origin, and length of the target representing the difference between two depths). The algorithm utilizes particle movement to identify the best way to reach the global or optimum solution. The algorithm's performance was evaluated on synthetic-examples with and without noise (5% and 10% levels) and also verified on a real dataset for mineral exploration from Canada. The results showed that the algorithm's stability and accuracy were not affected by the presence of noise and multi-models. Moreover, the field case results were consistent with the existing geological information, borehole data, and previously published outcomes.